Local community detection aims to find a cluster of nodes by exploring a small region of the network. Local community detection methods are faster than traditional global community detection methods because their runtime does not depend on the size of the entire network. However, most existing methods do not take the higher-order connectivity patterns crucial to the network into consideration. In this paper, we develop a new Local Community Detection method based on network Motif (LCD-Motif) which incorporates the higher-order network information. LCD-Motif adopts the local expansion of a seed set to identify the local community with minimal motif conductance, representing a generalization of the conductance metric for network motifs. In contrast to PageRank-like diffusion methods, LCD-Motif finds the community by seeking a sparse vector in the span of the local spectra, such that the seeds are in its support vector. We evaluate our approach using real-world datasets across various domains and synthetic networks. The experimental results show that LCD-Motif can achieve a higher performance than state-of-the-art methods.

The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel (BSP) computing model named BSP-based Parallel Graph Mining (BPGM). This system has four sets of parallel graph mining algorithms programmed in the BSP parallel model and a well-designed workflow engine optimized for cloud computing to invoke these algorithms. Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.

Recently, complex networks have attracted considerable research attention. Community detection is an important problem in the field of complex networks and is useful in a variety of applications such as information propagation, link prediction, recommendation, and marketing. In this study, we focus on discovering overlapping community structures by using link partitions. We propose a Latent Dirichlet Allocation (LDA)-Based Link Partition (LBLP) method, which can find communities with an adjustable range of overlapping. This method employs the LDA model to detect link partitions, which can calculate the community belonging factor for each link. On the basis of this factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of the proposed solution by using both real-world and synthesized networks. The experimental results demonstrate that the approach can find a meaningful and relevant link community structure.